High resolution unstructured meshes combined with scalable solvers enable automated anatomical model generation and strongly coupled electromechanical simulations at an unprecedented level of detail.
Computational models of cardiac electromechanics (EM) are increasingly being applied to clinical problems, with patient-specific models being generated from high fidelity imaging and used to simulate patient physiology, pathophysiology and response to treatment. Current structured meshes are limited in their ability to fully represent the detailed anatomical data available from clinical images and capture complex and varied anatomy with limited geometric accuracy. In this paper, we review the state of the art in image-based personalization of cardiac anatomy for biophysically detailed, strongly coupled EM modeling, and present our own tools for the automatic building of anatomically and structurally accurate patient-specific models. Our method relies on using high resolution unstructured meshes for discretizing both physics, electrophysiology and mechanics, in combination with efficient, strongly scalable solvers necessary to deal with the computational load imposed by the large number of degrees of freedom of these meshes. These tools permit automated anatomical model generation and strongly coupled EM simulations at an unprecedented level of anatomical and biophysical detail.
Crozier et al. (Wed,) conducted a review in Cardiac electromechanics modeling. Image-based personalization of cardiac anatomy was evaluated. High resolution unstructured meshes combined with scalable solvers enable automated anatomical model generation and strongly coupled electromechanical simulations at an unprecedented level of detail.